Web-Based Benchmark for Keystroke Dynamics Biometric Systems: A Statistical Analysis
This work addresses the problem of over-optimistic evaluations in biometric security research by offering a more realistic benchmark for researchers and practitioners, though it is incremental as it builds on existing methods with new data.
The paper tackles the lack of realistic evaluation in keystroke dynamics biometrics by providing a new web-based dataset with both imposed and chosen login-password pairs, collected in uncontrolled environments, and conducts a statistical analysis on factors like password size and fusion schemes, revealing new insights into performance under realistic conditions.
Most keystroke dynamics studies have been evaluated using a specific kind of dataset in which users type an imposed login and password. Moreover, these studies are optimistics since most of them use different acquisition protocols, private datasets, controlled environment, etc. In order to enhance the accuracy of keystroke dynamics' performance, the main contribution of this paper is twofold. First, we provide a new kind of dataset in which users have typed both an imposed and a chosen pairs of logins and passwords. In addition, the keystroke dynamics samples are collected in a web-based uncontrolled environment (OS, keyboards, browser, etc.). Such kind of dataset is important since it provides us more realistic results of keystroke dynamics' performance in comparison to the literature (controlled environment, etc.). Second, we present a statistical analysis of well known assertions such as the relationship between performance and password size, impact of fusion schemes on system overall performance, and others such as the relationship between performance and entropy. We put into obviousness in this paper some new results on keystroke dynamics in realistic conditions.